(3) Our DiPS training architecture is comprised of pretrained SST and classifier models that remain frozen. During training, the weights a U-Net style localization network are updated, and learn to produce full resolution foreground and background CAMs through parametric upscaling [1]}. The loss is composed of local and global constraints. The local constraint uses sampled pixel-wise pseudo-labels to guide the network via partial cross-entropy loss. In addition, a CRF loss [2]} is employed to yield consistent CAMs that respect object boundaries. To ensure that the correct object is localized, we maximize the classifier response w.r.t. the image-class label, over the image obtained by masking the input image with the output foreground CAM.
